System and method for analyzing and visualizing local clinical features

ABSTRACT

A system and method for analyzing and visualizing a local feature of interest includes access of a clinical image dataset comprising clinical image data acquired from a patient, identification of a region of interest (ROI) from the clinical image dataset, and extraction of at least one local feature corresponding to the ROI. The system and method also include definition of a local feature dataset comprising data representing at least one local feature, access of a pre-computed reference dataset comprising image data representing an expected value of the at least one identified derived characteristic of interest, and comparison of the characteristic dataset to the pre-computed reference dataset. Further, the system and method include calculation of at least one deviation metric from the comparison and output of a visualization of the at least one deviation metric.

BACKGROUND OF THE INVENTION

Embodiments of the invention relate generally to medical diagnosis and,more particularly, to a system and method for analyzing and visualizinglocal clinical features.

One type of medical condition or disease that is of interest to themedical community is neurodegenerative disorders (NDDs), such asAlzheimer's disease and Parkinson's disease. Alzheimer's diseasecurrently afflicts tens of millions of people worldwide, and accountsfor a majority of dementia cases in patients. Further, there is not, asof yet, any known cure. The economic and social costs associated withAlzheimer's disease are significant, and are increasing over time.

However, NDDs may be challenging to treat and/or study because they areboth difficult to detect at an early stage, and hard to quantify in astandardized manner for comparison across different patient populations.In response to these difficulties, investigators have developed methodsto determine statistical deviations from normal patient populations. Oneelement of the detection of NDD is the development of age and tracersegregated normal databases. Comparison to these normals can only happenin a standardized domain, e.g., the Talairach domain or the MontrealNeurological Institute (MNI) domain. The MNI defines a standard brain byusing a large series of magnetic resonance imaging (MRI) scans on normalcontrols. The Talairach domain references a brain that is dissected andphotographed for the Talairach and Tournoux atlases. In both theTalairach domain and the MNI domain, data must be mapped to therespective standard domain using registration techniques. Currentmethods that use a variation of the above method include tracersNeuroQ®, Statistical Parametric matching (SPM), 3D-sterotactic surfaceprojections (3D-SSP), and so forth.

Once a comparison has been made, an image representing a statisticaldeviation of the anatomy is displayed, allowing a viewer to make adiagnosis based on the image. Making such a diagnosis is a veryspecialized task and is typically performed by highly-trained medicalimage experts. However, even such experts can only make a subjectivecall as to the degree of severity of the disease. Due to this inherentsubjectivity, the diagnoses tend to be inconsistent andnon-standardized. It may, therefore, be desirable to increase theconsistency and standardization of such diagnoses. It may also bedesirable to incorporate additional data, including non-image data, toprovide a holistic approach to patient diagnosis.

BRIEF DESCRIPTION OF THE INVENTION

In accordance with one aspect of the invention, a computer readablestorage medium has stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toaccess a set of patient image data acquired from a patient and identifya target region of interest (ROI) from the set of patient image data.The instructions also cause the computer to define a feature datasetfrom the set of patient image data, the feature dataset representing alocal feature of interest extracted from the target ROI, access a set ofpre-computed image data, and define a reference dataset from the set ofpre-computed image data, the reference dataset representing an expectedvalue of the local feature of interest for the target ROI. Further, theinstructions cause the computer to compare the feature dataset to thereference dataset, generate a deviation metric based on the comparison,and output a visualization of the deviation metric.

In accordance with another aspect of the invention, a method includesaccessing a clinical image dataset comprising clinical image dataacquired from a patient, identifying a first ROI from the clinical imagedataset, and extracting at least one local feature corresponding to thefirst ROI. The method also includes defining a local feature datasetcomprising data representing the at least one local feature, accessing apre-computed reference dataset comprising image data representing anexpected value of the at least one local feature, and comparing thelocal feature dataset to the pre-computed reference dataset. Further,the method includes calculating at least one deviation metric from thecomparison and outputting a visualization of the at least one deviationmetric.

In accordance with yet another aspect of the invention, a system foranalyzing image data includes a patient database having stored thereonimage data acquired from a patient, a reference database having storedthereon pre-computed image data acquired from a reference population,and a processor. The processor is programmed to access a set of patientdata from the patient database, identify an ROI from the set of patientdata, extract a local feature corresponding to the ROI, and define a setof patient feature data from the set of patient data representing thelocal feature. The processor is further programmed to

access a set of pre-computed feature data from the reference databaserepresenting the local feature, compare the set of patient feature datato the set of pre-computed feature data, generate a deviation map fromthe comparison, and output a visualization of the deviation map. Thesystem further includes a graphical user interface (GUI) configured todisplay the deviation map for the local feature.

Various refinements of the features noted above may exist in relation tovarious aspects of the present invention. Further features may also beincorporated in these various aspects as well. These refinements andadditional features may exist individually or in any combination. Forinstance, various features discussed below in relation to one or more ofthe illustrated embodiments may be incorporated into any of theabove-described aspects of the present invention alone or in anycombination. Again, the brief summary presented above is intended onlyto familiarize the reader with certain aspects and contexts of thepresent invention without limitation to the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate preferred embodiments presently contemplated forcarrying out the invention.

In the drawings:

FIG. 1 is a block diagram of an exemplary processor-based device orsystem in accordance with one embodiment of the present invention;

FIG. 2 is a block diagram of an exemplary data acquisition andprocessing system in accordance with one embodiment of the presentinvention;

FIG. 3 is a flow chart of an exemplary method for preparing image datafor feature extraction in accordance with one embodiment of the presentinvention;

FIG. 4 is a flow chart of an exemplary method for creating a corticalthickness map from brain image data in accordance with one embodiment ofthe present invention;

FIG. 5 is a flow chart of an exemplary method for generating deviationmaps in accordance with one embodiment of the present invention;

FIG. 6 is an exemplary visual mapping of cortical thickness data on aninflated brain surface in accordance with one embodiment of the presentinvention;

FIG. 7 is a block diagram representative of the division of referencedata into standardized databases in accordance with one embodiment ofthe present invention;

FIG. 8 is a flow chart of an exemplary diagnosis method in accordancewith one embodiment of the present invention;

FIG. 9 is a flow chart of an exemplary method for creating and analyzingdeviation data in accordance with one embodiment of the presentinvention;

FIG. 10 is a flow chart of a method for diagnosing a patient based oncomparison of a patient deviation map to reference deviation maps inaccordance with one embodiment of the present invention;

FIG. 11 is a flow chart of an exemplary method for generating acomposite deviation map indicative of both structural and functionaldeviation in accordance with one embodiment of the present invention;

FIG. 12 is a flow chart of a method for generating image deviationscores for a patient in accordance with one embodiment of the presentinvention;

FIG. 13 is a flow chart of a method for generating non-image deviationscores for a patient in accordance with one embodiment of the presentinvention;

FIG. 14 is a flow chart of an exemplary method for generating a visualrepresentation of patient deviation data based on deviation scores inaccordance with one embodiment of the present invention;

FIG. 15 illustrates an exemplary visual representation of a variety ofpatient deviation data in accordance with one embodiment of the presentinvention;

FIG. 16 is a flow chart of an exemplary visualization method inaccordance with one embodiment of the present invention;

FIG. 17 is a flow chart of a different exemplary visualization method inaccordance with one embodiment of the present invention;

FIG. 18 is a diagram of an automatic comparison workflow to determine aseverity index in accordance with one embodiment of the presentinvention;

FIG. 19 is a flow chart of an exemplary method for calculating acombined disease severity score in accordance with one embodiment of thepresent invention;

FIG. 20 is a block diagram generally illustrating a process forcomparing patient data to standardized data for a plurality of diseasetypes and severity levels in accordance with one embodiment of thepresent invention;

FIG. 21 illustrates a plurality of representative reference deviationmaps that may be contained in a reference library or database of suchdeviation maps in accordance with one embodiment of the presentinvention; and

FIG. 22 illustrates additional representative reference deviation mapsthat may be contained in a reference library or database of deviationmaps in accordance with one embodiment of the present invention.

FIG. 23 is a flowchart illustrating a technique for visualization andanalysis of a local feature associated with a clinical image dataset inaccordance with another embodiment of the present invention.

FIG. 24 illustrates an exemplary visual representation of a graphicaluser interface (GUI) for displaying a visualization of deviation data inaccordance with one embodiment of the present invention.

DETAILED DESCRIPTION

One or more specific embodiments of the present invention will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

When introducing elements of various embodiments of the presentinvention, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Moreover, while the term “exemplary” may be used herein in connection tocertain examples of aspects or embodiments of the presently disclosedtechnique, it will be appreciated that these examples are illustrativein nature and that the term “exemplary” is not used herein to denote anypreference or requirement with respect to a disclosed aspect orembodiment. Further, any use of the terms “top,” “bottom,” “above,”“below,” other positional terms, and variations of these terms is madefor convenience, but does not require any particular orientation of thedescribed components.

Turning now to the drawings, and referring first to FIG. 1, an exemplaryprocessor-based system 10 for use in conjunction with the presenttechnique is depicted. In one embodiment, the exemplary processor-basedsystem 10 is a general-purpose computer, such as a personal computer,configured to run a variety of software, including software implementingall or part of the presently disclosed techniques, including the methodsand functionality described throughout the instant disclosure.Alternatively, in other embodiments, the processor-based system 10 maycomprise, among other things, a mainframe computer, a distributedcomputing system, or an application-specific computer or workstationconfigured to implement all or part of the present techniques based onspecialized software and/or hardware provided as part of the system.Further, the processor-based system 10 may include either a singleprocessor or a plurality of processors to facilitate implementation ofthe presently disclosed functionality.

In general, the exemplary processor-based system 10 includes amicrocontroller or microprocessor 12, such as a central processing unit(CPU), which executes various routines and processing functions of thesystem 10. For example, the microprocessor 12 may execute variousoperating system instructions as well as software routines configured toeffect certain processes stored in or provided by a manufactureincluding a computer readable-medium, such as a memory 14 (e.g., arandom access memory (RAM) of a personal computer) or one or more massstorage devices 16 (e.g., an internal or external hard drive, asolid-state storage device, CD-ROM, DVD, or other storage device). Inaddition, the microprocessor 12 processes data provided as inputs forvarious routines or software programs, such as data provided inconjunction with the present techniques in computer-basedimplementations.

Such data may be stored in, or provided by, the memory 14 or massstorage device 16. Alternatively, such data may be provided to themicroprocessor 12 via one or more input devices 18. As will beappreciated by those of ordinary skill in the art, the input devices 18may include manual input devices, such as a keyboard, a mouse, or thelike. In addition, the input devices 18 may include a network device,such as a wired or wireless Ethernet card, a wireless network adapter,or any of various ports or devices configured to facilitatecommunication with other devices via any suitable communicationsnetwork, such as a local area network or the Internet. Through such anetwork device, the system 10 may exchange data and communicate withother networked electronic systems, whether proximate to or remote fromthe system 10. It will be appreciated that the network may includevarious components that facilitate communication, including switches,routers, servers or other computers, network adapters, communicationscables, and so forth.

Results generated by the microprocessor 12, such as the results obtainedby processing data in accordance with one or more stored routines, maybe stored in a memory device, may undergo additional processing, or maybe provided to an operator via one or more output devices, such as adisplay 20 and/or a printer 22. Also, based on the displayed or printedoutput, an operator may request additional or alternative processing orprovide additional or alternative data, such as via the input device 18.As will be appreciated by those of ordinary skill in the art,communication between the various components of the processor-basedsystem 10 may typically be accomplished via a chipset and one or morebusses or interconnects which electrically connect the components of thesystem 10. Notably, in certain embodiments of the present techniques,the exemplary processor-based system 10 may be configured to facilitatepatient diagnosis, as discussed in greater detail below.

An exemplary system 30 for acquiring and processing data is illustratedin FIG. 2 in accordance with one embodiment of the present invention.The system 30 includes a data processing system 32 configured to providevarious functionality. It should be noted that, in one embodiment, thedata processing system 32 may include a processor-based system, such assystem 10, having any suitable combination of hardware and/or softwarecode, routines, modules, or instructions adapted to perform thepresently discussed functionality, including performance of varioussteps of the methods described elsewhere herein. It should be noted thatsuch software routines may be embodied in a manufacture (e.g., a compactdisc, a hard drive, a flash memory, RAM, or the like) and configured tobe executed by a processor to effect performance of the functionalitydescribed herein.

The system 30 may also include one or more data acquisition systems 34for collecting data from, or regarding, a patient 36. The patient datamay include one or both of image data and non-image data, and mayinclude any of static data, dynamic data, and longitudinal data. Invarious embodiments, the data acquisition systems 34 may include patientmonitors, imaging systems of various modalities, computers, or any othersuitable systems capable of collecting or receiving data regarding thepatient 36. For instance, the data acquisition systems 34 may include,among others, an X-ray system, a computed tomography (CT) imagingsystem, a magnetic resonance (MR) imaging system, a positron emissiontomography (PET) imaging system, a single photon emission computedtomography (SPECT) imaging system, a digital tomosynthesis imagingsystem, an electroencephalography (EEG) system, an electrocardiography(ECG or EKG) system, an electromyography (EMG) system, an electricalimpedance tomography (EIT) system, an electronystagmography (ENG)system, a system adapted to collect nerve conduction data, or somecombination of these systems.

Various components of the system 30, including the data processingsystem 32 and the data acquisition systems 34, may be connected to oneanother via a network 38 that facilitates communication between suchcomponents. The system 30 may also include one or more databases, suchas databases 40 and 42, for storing data, such as data collected by thedata acquisition systems 34 and data used by or generated from the dataprocessing system 32, including both patient data and standardizedreference data, as discussed in greater detail below. Additionally, thedata processing system 32 may receive data directly from the dataacquisition systems 32, from the databases 40 and 42, or in any othersuitable fashion.

In some embodiments, it may be desirable to analyze one or more featuresof interest from image data to facilitate diagnosis of a patient withrespect to one or more disease types or disease severity levels.Accordingly, an exemplary method 48 for preparing image data for featureextraction is generally illustrated in FIG. 3 in accordance with oneembodiment of the present invention. Image data 50 may be obtained fromvarious sources, such as one or more of the data acquisition systems 34,the databases 40 or 42, or the like. Further, such image data may berelated to a particular patient, such as the patient 36, or to one ormore reference individuals of population sample. The method 48 mayinclude various steps, such as steps 52, 54, 56, 58, and 60, forprocessing, registering, and extracting features of interest.

In the presently illustrated embodiment, the method 48 includes a step52 of preprocessing the image data. Such preprocessing may include ahost of sub-processes, such an intensity correction, resembling,filtering, and so forth. In steps 54 and 56, anatomical markers in theimage data 50 may be detected, and an image grid may be created. Basedon the anatomical markers and the image grid, the data may undergoregistration in a step 58. Following registration, features of interestin the image data 50 may be extracted in a step 60. While certainexemplary steps of the method 48 are presently described, it should benoted that the image data 50 may undergo registration or featureextraction through fewer, different, or additional steps in fullaccordance with the present technique.

In one embodiment, the image data 50 includes one or more images of ahuman brain that may be mapped to a Talairach coordinate system. In suchan embodiment, the image data of the human brain, which may include anMR image or some other image, may be normalized to correct intensityvariations and resampled, such as to a 256.times.256.times.128 internalmatrix, for further processing. Also, in such an embodiment, theanterior and posterior commissures (AC-PC) of the brain image and otheranatomical reference points may be identified to facilitate Talairachregistration. The brain images of the image data 50 may be elasticallyregistered, such as through warping, to the Talairach coordinate system,to facilitate later representation, analysis, and diagnostics.

It should be noted that the particular features that are of interest inthe image data may vary depending on a particular disease or conditionof interest. For example, in diagnosing neurological conditions, it maybe useful to extract certain features of brain image data to facilitatediagnosis. Further, in some embodiments, it may be desirable todetermine the thickness of the cerebral cortex of a patient or of one ormore reference individuals. Accordingly, an exemplary method 64 fordetermining the cortical thickness of a brain from patient image data orreference image data, and for generating a cortical thickness map, isprovided in FIG. 4 in accordance one embodiment of the presentinvention.

The method 64 may include a step 68 of segmenting brain tissue in imagedata 66 from other anatomical structures outside the brain, such as theskull. Further, in step 70, white matter of the brain and subcorticalregions, such as ventricles may be segmented from the gray matter of thecerebral cortex. As the relative image intensities of the brain whitematter and the other soft tissues may be very close or overlapped, inone embodiment the segmented brain may be manually edited to removeunwanted remaining tissue, or to restore inadvertently deleted corticaltissue, generally corresponding to a step 72. Further white mattersegmentation, surface fitting, and smoothing may be performed in steps74 and 76. In a step 78, the pial surface (i.e., the outside surface ofthe brain gray matter) may be detected. It should be noted that the pialsurface generally includes numerous gyri and sulci, but may beconsidered to be smooth regionally to facilitate processing. The pialsurface may be detected in various matters, such as through use of adeformable model or dilation from the surface of the white matter. Thethickness of the cerebral cortex (i.e., the cortical thickness) may becalculated in a step 80, and a cortical thickness map visually depictingthe cortical thickness may be created in a step 82.

In some embodiments, standardized reference cortical thickness maps maybe calculated from image data collected from other persons or groups ofpersons (e.g., normal persons, persons diagnosed with Alzheimer'sdisease (AD), persons diagnosed with Parkinson's disease (PD), personsdiagnosed with frontotemporal dementia (FID), and so forth), and storedin large databases, such as those collected by the Alzheimer's DiseaseNeuroimaging Initiative (ADNI). Such standardized maps may serve asreference image data with respect to patient cortical measurements, andmay be grouped and standardized according to any desired characteristic.For instance, in one embodiment, such data may be standardized based ona demographic characteristic, such as the race, gender, or age of thepersons from which the data was collected. Such standardized data allowsfor the computation of average cortical thickness of normal patients andthe thickness distribution across different function regions of thebrain that affect memory, movement, speech, language, hearing, vision,sensation, emotion, and so forth. The average cortical thickness mapsmay be created from the reference image data, and also standardizedaccording to age, gender, or race distributions, or according to anyother characteristic of interest. While certain presently disclosedembodiments are described with respect to brain features, such ascortical thickness, it will appreciated that the present techniques maybe applied more generally to any features of interest, including thoseof image data of other anatomical regions besides the brain.

In some instances, it may be desirable to also generate anatomicaldeviation maps, such as cortical thickness deviation maps, indicative ofdifferences between a patient anatomical region and a referenceanatomical region. As such, an exemplary method 88 for generatingdeviation maps from standardized reference data is illustrated in FIG. 5in accordance with one embodiment of the present invention. In thepresently illustrated embodiment, reference image data 90 isstandardized in a step 92. As noted above, reference image data may becollected from a population of individuals and grouped or standardizedaccording to one or more desired characteristics, such as age, gender,or race. While the presently illustrated embodiment is described withrespect to image data, it is noted that reference non-image data andpatient non-image data may also, or instead, be used to generate thedeviation maps discussed herein in full accordance with the presenttechnique.

The method 88 may include a step 94 of selecting a subset of thestandardized reference image data based on a patient characteristic. Forinstance, if a patient is a sixty-five-year-old woman, a subset of thestandardized reference image data grouped to include reference imagespertaining to women between sixty and seventy years of age may be morerelevant for comparative purposes than a group of standardized referenceimages composed of data collected from men between twenty and thirtyyears of age. Once a desired group of standardized image data isselected, the matched standardized image data 96 may be compared toimage data 100 of the patient in a step 98. In other embodiment,non-image data of the patient may instead or also be compared to matchedstandardized non-image data, as described above. Additionally, thevarious data may be processed and standardized in any suitable manner tofacilitate such comparisons.

Based on such comparison, a patient deviation map representative of thedifference between the patient image data 100 and the standardized imagedata 96 may be generated in step 102. For example, with respect tocortical thickness, a patient cortical thickness map may be obtainedthrough a comparison of the patient cortical thickness map with astandardized cortical thickness map based on a representative populationof normal individuals. Consequently, in one embodiment, the patientcortical thickness deviation map may generally illustrate differences ofthe cortical thickness of the patient with respect to normal people ofsimilar age, sex, or race. The deviation maps described herein may begenerated through any suitable techniques. In one embodiment, adeviation map is a visual representation in which each point of the maprepresents a z-score generally corresponding to the number of standarddeviations (based on a population) in the difference between a patientvalue and the average value (of the population) for that point. Althoughsuch deviation maps may be calculated from image data, it is noted thatdeviation maps may be created using one or more of numerical data, textdata, waveform data, image data, video data, or the like.

The various anatomical region maps and deviation maps described hereinmay be visualized to facilitate further analysis or diagnosis. Forinstance, any or all of the standardized cortical thickness maps, thepatient cortical thickness maps, the patient cortical thicknessdeviation maps, or standardized cortical thickness deviation maps (asdescribed below) may be expressed as surface matrices, and can bedisplayed or overlaid on a three-dimensional (3D) brain surface, a pialsurface, or an inflated brain surface.

By way of further example, such an expression is illustrated in FIG. 6in accordance with one embodiment of the present invention.Particularly, cortical thicknesses or deviations may be depicted on aninflated brain surface 108, as illustrated within window 110. Variousregions of the brain 108 may be color coded according to a scale 112 torepresent the cortical thickness, or deviation from normal thickness, tofacilitate user-understanding of the represented anatomical information.

FIG. 23 illustrates an alternative technique 502 for visualizing andanalyzing features or characteristics of interest from image data, inaccordance with an embodiment of the invention. Technique 502 accessesimage data 504 acquired from a particular patient, such as patient 36(FIG. 2). The medical data may include image data acquired from anynumber of data acquisition systems, such as, for example, an X-raysystem, an ultrasound system, a CT system, an MR system, a PET system,and/or a SPECT system.

At step 506, one or more clinical areas or regions of interest (ROI) areselected from the image data 504. As used herein, ROI means any singleor multi-dimensional area of interest, such as, for example, an area ora volume. Each ROI may be selected manually, semi-automatically, orautomatically according to various embodiments using any combination ofavailable image manipulation tools such as ROI selection, registration,segmentation, contouring, etc. For example, a clinician may select anROI using an input device (e.g., input device 18 of FIG. 1) by drawing acontour around the ROI in an image of the patient on a display (e.g.,display 20 of FIG. 1). Alternatively, a computer algorithm may be usedto automatically define one or more ROIs in image data 504. Whiletechnique 502 is described herein with respect to selection of an ROI,one skilled in the art will recognize that technique 502 is equallyapplicable to a selection of a volume of interest (VOI) from the imagedata.

One or more local features of interest are selected at step 508 and datacorresponding to the local feature(s) of interest is extracted from eachROI. Local features represent quantitative parameters that are derivedfrom a subset of the medical image dataset corresponding to the ROI. Forexample, for a given ROI, local features may include any number ofshape-based parameters (e.g., corners, roundness, symmetry, orientation,eccentricity, center of mass, boundaries, moments, etc.), size-basedparameters (e.g., perimeter, area, max/min radii, etc.), and/ormaterial- or texture-based parameters (e.g., edge-ness, homogeneity,adjacency, edge density, extreme density, texture transforms, etc.).Further, local features may correspond to any anatomical features orfunctional features present within image data. Local features may beextracted manually, semi-automatically, or automatically from theclinical ROI, according to various embodiments. Local features arenon-pixel specific. Instead, each local feature is derived from imagedata representing a group of pixels.

Technique 502 accesses reference data corresponding to each ROI at step510. The reference data is selected to correspond to the extracted localfeatures and represents baseline information for each local feature. Forexample, the reference data may comprise known values for the localfeature acquired from healthy or normal anatomy. According to oneembodiment, the reference data is selected from a pre-computed database,such as, for example, a standardized reference database that includesimage data collected from other persons or groups of persons (e.g.,normal persons or known abnormal persons, such as persons diagnosed witha specific disease). The reference data may be selected from thestandardized reference database based on application of aprocessing-based filter that filters reference data corresponding to theROI.

At step 512, technique 502 calculates one or more deviation metrics torepresent a deviation between the patient data and the reference data.The deviation metric captures the extent of the deviation of theextracted local features with respect to the reference data. Thisanalysis may be performed on a single ROI within the patient data set oron multiple ROIs for each extracted local feature. In the single ROIexample, a single value for the extracted local feature (calculated fromone ROI) is compared against corresponding feature data from thereference dataset. The extent of the deviation from the expected valuebased on the reference is calculated. In the multiple ROI example, localfeatures corresponding to two or more ROIs are compared to one or morereference regions. Specifically, a set of multiple data points of theextracted local feature (from one or more ROIs) is compared against areference set of data points. For example, an analysis may compareextracted local features of ROIs representing several cysts of interestto corresponding local features of a dataset acquired from a number ofreference cysts to determine whether the cysts of interest are made upof a different material than the reference cysts.

Any number of techniques may be applied to calculate metrics thatexpress the deviation of the extracted local features with respect tothe reference dataset. For example, according to one embodiment, az-score deviation of a local characteristic of interest is calculated ina similar manner as described with respect to step 102 of FIG. 5. Atstep 514 technique 502 outputs a visualization of the deviation of theextracted local features similar to region 298 of FIG. 15. Accordingly,the extracted local features allow for creation of a visualrepresentation of non-image quantitative info derived from the image.

In some embodiments, the visual representations output at step 514 (FIG.23) may be displayed on a graphical user interface (GUI) 516 asillustrated in FIG. 24. GUI 516 includes a region 518 for visualizationof deviation maps, similar to region 298 of FIG. 15. A common colorscale 520 is also provided to give meaning to the coding of the cells inthe deviation map. That is, scale 520 normalizes the scaled values toone another such that deviation may be compared across local features.Thus, local features that deviate greatly from the reference data aredisplayed at a first end 522 of color scale 520 while local featuresthat closely correlate to the reference data are displayed at a secondend 524 of color scale 520, opposite first end 522.

GUI 516 also includes a region 526 for visualization of patient imagedata, such as image data 50 (FIG. 3), image data 66 (FIG. 4), image data100 (FIG. 5), data 148 (FIG. 8), image data 242 (FIG. 12), as examples.A number of data regions 528, 530, 532, 534 are also included in GUI 516to display numeric and textual data, according to various embodiments,including patient image data, reference image data, deviation scores,clinical tests, patient-specific data, reference-specific data, asexamples. Optionally, one or more of regions 528-534 may be configuredas a control panel to permit a user to input and/or select data throughinput fields, dropdown menus, etc. It is noted that the arrangement ofGUI 516 is provided merely for explanatory purposes, and that other GUIarrangements, field names, and visual outputs may take different forms.Additional display techniques may also include temperature gauges,graphs, dials, font variations, annotations, and the like.

Additionally, reference data may be classified and sorted intostandardized databases, such as through an exemplary method 118generally depicted in FIG. 7 in accordance with one embodiment of thepresent invention. The method 118 may include accessing reference data120, which may include known population image data, and classifying suchdata in a step 122. For example, the reference data 120 may beclassified into various groups, such as data 124 for normal patients;data 126 for patients clinically diagnosed with a first condition, suchas Alzheimer's disease (AD); data 128 for patients diagnosed with asecond condition, such as frontotemporal dementia (FTD); and data 130for patients diagnosed with other conditions, such as Parkinson'sdisease (PD), Huntington's disease (HD), multi-infarct dementia (MID),diffuse cortical Lewy body disease (DLBD), normal pressurehydrocephalus, progressive supranuclear palsy (PSP), or the like. Whilecertain brain disorders, brain image data, and brain deviation maps arepresently discussed for the sake of explanation, it is again noted thatthe use of the present techniques with other, non-neurological data anddisorders is also envisaged. The data 124, 126, 128, and 130 may bestored in respective databases 132, 134, 136, and 138. Such databasesmay be stored in one or more memory devices or in other suitable media.

An exemplary method 144 for diagnosing a patient based at least in parton the foregoing data is illustrated in FIG. 8 in accordance with oneembodiment of the present invention. The method 144 may include creatinga patient map of a structural feature in a step 146, based on receivedpatient data 148. In one embodiment related to brain disorders, thepatient map created in step 146 may include a patient cortical thicknessmap. In a step 150, a normalized map of a structural feature is createdbased on the data 124 for normal patients. For instance, a standardizedcortical thickness map for normal patients may be generated in thisstep. Although the presently illustrated embodiment is discussed withreference to maps of structural features, it is noted that maps of otherfeatures, such as functional or metabolic features, may also or insteadbe used in full accordance with the presently disclosed technique.

In a step 152, reference condition maps (e.g., average maps or otherreference maps) of the structural feature may be created for eachdiagnosed condition or disorder, based on the reference data 126, 128,and 130 collected with respect to individuals of a population diagnosedwith such conditions. For example, in one embodiment, representativeaverage cortical thickness map may be calculated for each brain disorderof interest, such as AD, FTD, PD, or the like. Additionally, averagemaps (or other reference maps) corresponding to various severity levelswithin a disease type may also be generated. Thus, multiplerepresentative or average maps may be created for each diagnosedcondition or disease type.

The method 144 may also include a step 154 of comparing the patient andnormal maps, and a step 156 of comparing the reference condition andnormal maps. In one embodiment, the method 144 may include a step 158 ofcomparing one or more patient deviation maps (which may be generatedfrom the comparison of step 154) with one or more disease referencedeviation maps (which may be generated from the comparison of step 156).It is noted that the above-referenced maps, as well as other maps anddata described herein, may be standardized into one or more common orsimilar formats to facilitate analysis and comparison. Also, it will beappreciated that the various maps described herein may be stored in oneor more databases to facilitate subsequent data analysis. Additionally,any or all of the foregoing comparisons may be performed eitherautomatically by a data processing system (e.g., system 32), or by ahealthcare provider (e.g., a doctor), or by some combination thereof, tofacilitate automatic or manual diagnosis of the patient in a step 160.Such diagnosis may also be based on additional data, such as clinicaldata 162, laboratory data, patient history, patient vital signs, resultsof various tests (e.g., functional tests, cognitive tests, neurologicaltests, or genetic tests), and so forth. Additionally, in a step 164 ofthe method 144, a report 166 may be output to a database 168 forstorage, or to a user 170 in a human-readable format.

Based on the patient and reference data and maps discussed above,numerous reference and patient deviation data and maps may be created.By way of example, an exemplary method 172 for creating and analyzingsuch deviation data is depicted in FIG. 9 in accordance with oneembodiment of the present invention. The method 172 includes accessingreference cortical thickness data for: noimal patients without diagnosedbrain disorders (data 174), patients clinically diagnosed with AD (data176), patients diagnosed with FTD (data 178), and patients diagnosedwith PD (data 180). The method 172 may also include accessing patientcortical thickness data 182. It will be appreciated that, in otherembodiments, the method 172 may access reference cortical thickness datafor other brain disorders, which may be processed in a manner similar tothose explicitly discussed in the present example. Indeed, the presentprocessing techniques may also be applied to other disorders unrelatedto the brain.

In a step 184, the normal data 174 may be compared to each of the otherdata 176, 178, 180, and 182, to generate patient deviation data 186, ADdeviation data 188, FTD deviation data 190, and PD deviation data 192,all of which may represent deviations from the normal data 174. Suchdeviation data may include structural deviation maps, such as corticalthickness deviation maps, representative of differences between thepatient data and the disease type reference data, on the one hand, andthe normal reference data on the other. Additionally, the deviation datamay also include functional deviation maps indicative of functional,rather than structural, differences between the patient (or referencedata indicative of reference disease types) and normal individuals. Insome embodiments, structural deviation maps may include corticalthickness deviation maps, and functional deviation maps may includecerebral blood flow rate deviation maps or metabolic rate deviationmaps.

In step 194, such deviation data may be analyzed. For instance, in oneembodiment, a patient cortical thickness deviation map may be comparedto representative reference cortical thickness deviation maps for eachof the above noted brain disorders to facilitate diagnosis of thepatient with respect to one or more of such brain disorders.Additionally, reference clinical data 196, patient clinical data 198,and other data 200 may also be analyzed by a data processing system or auser to facilitate diagnosis. In one embodiment, such analysis mayinclude pattern matching of patient maps and reference maps, andconfidence levels of such matching may be provided to a user. Finally,results 202 of the analysis may be output to storage or to a user.

A method 194 for analyzing the data discussed above and diagnosing apatient is illustrated in FIG. 10 in accordance with one embodiment ofthe present invention. In a step 208, one or more patient deviationmaps, which may include a structural deviation map (e.g., a corticalthickness deviation map) or some other deviation map (e.g., a functionaldeviation map), may be compared to one or more reference deviation maps,such as those previously described. Notably, the reference deviationmaps may include deviation maps (e.g., functional deviation maps ormetabolic deviation maps or structural deviation maps) representative ofone or more disease types, as well as various severity levels of the oneor more disease types.

Based on such comparisons, one or more patient disease types and/ordisease severity levels may be identified in a step 210 and diagnosed ina step 212. In some embodiments, such as a fully automated embodiment,steps 210 and 212 may be combined. In other embodiments, however, theidentification and diagnosis may be performed as separate steps. Forinstance, the data processing system 32 may identify various potentialdisease types or severity levels and present the identified diseasetypes or severity levels to a user for diagnosis. A report 214 mayinclude an indication of the identified patient disease types orseverity levels, the diagnosis, or both.

In some embodiments, it may be desirable to combine indications offunctional deviations and structural deviations of a patient withrespect to reference data and to output such deviations in a visualmanner that facilitates efficient diagnosis of a patient by a healthcareprovider. Accordingly, an exemplary method 218 for generating acomposite deviation map indicative of both structural and functionaldeviation is depicted in FIG. 11 in accordance with one embodiment ofthe present invention. In the presently illustrated embodiment, themethod 218 includes steps 220 and 222 for accessing structural andfunctional data, respectively, for a patient. The patient structural andfunctional data may include various image and non-image data withrespect to an anatomical region of the patient. In one embodiment, theanatomical region may include the cerebral cortex of the patient.Additionally, the patient structural and functional data may includeimage data obtained from different imaging modalities.

The patient structural and functional data may be compared tostandardized reference structural and functional data, respectively, insteps 224 and 226. As noted previously, reference data may bestandardized according to any desired characteristics, such as, but notlimited to, age, gender, or race. Based on such comparisons, one or morestructural deviation maps for the patient may be generated in a step228, and one or more patient functional deviation maps may be generatedin a step 230. In one embodiment, the patient structural deviation mapmay indicate deviation of patient cortical thickness at one or moreparticular locations of the patient cerebral cortex with respect toexpected thickness represented by the standardized reference data. Inanother embodiment, the patient structural deviation map may begenerated via comparison of MR images of the patient and of thestandardized reference data. Also, in a neurological context, thepatient functional deviation map may indicate deviation of patient brainfunctioning, such as a cerebral blood flow rate or a metabolic rate,from standardized rates. It will, however, be appreciated that thedeviation maps may be generated based on a wide array of image dataand/or non-image data, as discussed above.

It is again noted that the patient structural deviation map maygenerally represent structural differences of an anatomical region ofthe patient with respect to standardized reference data for a similaranatomical region. For instance, in one embodiment, the patientstructural deviation map may include a cortical thickness deviation mapfor the patient with respect to standardized cortical thickness data,such as described above. In turn, the patient functional deviation mapmay represent non-structural differences between a patient anatomicalregion and a corresponding anatomical region of standardized data. Forexample, in some embodiments, the patient functional deviation map maybe indicative of differences in metabolic activity or other functionalactivity between the patient and standardized reference data. Tofacilitate easy and efficient communication of such differences to auser, a composite patient deviation map, indicative of both thefunctional and structural differences discussed above, may be created ina step 232.

The patient structural deviation map and the patient functionaldeviation map, along with any other additional deviation maps, may becombined in any suitable fashion to create the composite patientdeviation map. For instance, in one embodiment, the individual patientdeviation maps may be overlain to create a single composite patientdeviation map indicative of multiple deviations of the patient withrespect to standardized data. In another embodiment, the individualpatient functional and structural deviation maps may be combined throughan image fusion process. Particularly, in one embodiment, the patientstructural deviation map may be generated through comparison of patientimage data and standardized image data each of a first imaging modality,while the patient functional deviation map is generated from image data(of both the patient and standardized reference sources) obtainedthrough a second imaging modality different than the first. For example,structural deviations identified through comparison of MR images may becombined with functional deviations obtained from PET image data togenerate a single composite patient deviation map indicative of bothfunctional and structural deviations. In another embodiment, the patientstructural deviation map based on a first criterion (e.g., corticalthickness from MRI images) can be combined with the patient structuraldeviation map based on a second criterion (e.g., medial temporal lobeatrophy from CT images). In yet another embodiment, the patientfunctional deviation map based on a first criterion (e.g., FDG, a wellknown PET tracer uptake in PET images) can be combined with the patientfunctional deviation map based on a second criterion (e.g., uptake ofPIE, a well known tracer for beta-amyloid in PET images).

Additionally, different colors may be used to indicate and contraststructural differences and functional differences. For example, in oneembodiment, functional deviations may generally be depicted in acomposite patient deviation map by the color red, while structuraldeviations may generally be indicated through use of the color blue.Additionally, the magnitude of such deviations may be represented byvarious shades of red or blue to allow a doctor or other user to quicklyascertain patient deviations and the magnitudes of such deviations, aswell as to facilitate diagnosis of the patient. It will be appreciated,however, that other or additional colors may also be used to indicatethe different types of deviations and their relative magnitudes.

The method 218 may also include outputting the composite patientdeviation map in a step 234. In some embodiments, outputting thecomposite patient deviation map may include storing the compositepatient deviation map in a memory device. In other embodiments,outputting the composite patient deviation map may also, or instead,include providing the composite map to a user in a human-readableformat, such as by displaying the composite patient deviation map on adisplay or printing a physical copy of the composite patient deviationmap. Also, the presently illustrated embodiment is currently representedas a parallel process with respect to the generation of separate patientstructural and functional deviation maps. It is noted that, while thepresent exemplary method is described for explanatory purposes as aparallel process, the steps of any of the methods described herein maybe performed in any suitable manner, and are not limited to beingperformed in any particular order or fashion.

The extent of patient deviation from standardized data may also betranslated into one or more deviation scores, which may, in oneembodiment, be generated through the methods generally depicted in FIGS.12 and 13. An exemplary method 240 of FIG. 12 may include accessingpatient image data 242 and reference image data 244. Such image data maybe received from any suitable source, such as a database or an imagingsystem. Indeed, the image data 242 and 244 may include image data from avariety of modalities and collected from a wide range of sources. Thereference image data 244 may be standardized according to any desiredcharacteristics. For instance, in one embodiment, the reference imagedata 244 may generally represent features of normal individuals withcertain demographic characteristics (e.g., characteristics similar tothe patient). In a step 246, the patient image data 242 and thereference image data 244 may be compared to determine deviations of thepatient image data 242 from the reference image data 244. In oneembodiment, such differences may generally represent deviation (e.g.,structural or functional differences) of the patient from normalindividuals.

The method 240 may also include a step 248 of calculating one or morepatient image deviation scores for differences between the patient imagedata 242 and the reference image data 244. Such deviation scores may beindicative of an array of functional or structural deviations of thepatient with respect to reference image data, including deviations inmetabolic activity (e.g., fluorodeoxyglucose (FDG) metabolism, which maybe observed in PET images), physical anatomy (e.g., cortical thickness,which may be measured in MR images), and functional activity (e.g.,Pittsburgh Compound-B (PIB) measure, which may be determined from PETimages), to name but a few. The patient image deviation scores may becalculated in various manners, such as based on projection deviation,single pixel (2D) deviation, single voxel (3D) deviation, or on anyother suitable technique. The calculated patient image deviation scores250 may then be stored in a database 252, output to a user, or mayundergo additional processing in one or more further steps 254.

Turning to FIG. 13, an exemplary method 260 for calculating non-imagedeviation scores may include accessing patient non-image data 262 andreference non-image data 264. The non-image data may be received fromany suitable source, such as a database, a computer, or patient monitor.The patient non-image data 262 may include any non-image informationcollected for the purpose of diagnosing the patient, such as clinicaldata, laboratory data, patient history, patient vital signs, and thelike, and may also include results of functional tests, cognitive tests,neurological tests, genetic tests, and so forth. The non-image data 264may include similar data, which may be standardized based on one or morepopulation or sample characteristics. Further, in one embodiment, thepatient non-image data 262 and reference non-image data 264 may includeone or both of numeric data and enumerated data (each of which may becontinuous or discrete). The reference non-image data 264 may be datarepresentative of features of normal persons with desired demographiccharacteristics (e.g., characteristics similar to the patient). In astep 266, the patient non-image data 262 may be compared to thereference non-image data 264 to identify differences between the data.In one embodiment, such differences may generally represent deviation(e.g., structural or functional differences) of the patient from normalindividuals.

Additionally, the method 260 may include a step 268 of calculating oneor more patient non-image deviation scores for differences between thepatient non-image data 262 and the reference non-image data 264. It isnoted that various techniques may be used to calculate the patientnon-image deviation scores, including z-score deviation or distributionanalysis. Of course, it will be appreciated that other calculationtechniques may also or instead be employed in other embodiments. Thecalculated patient non-image deviation scores 270 may be stored in adatabase 272, output to a user, or may undergo additional processing inone or more further steps 274.

An exemplary method 280 for accessing patient deviation scores andgenerating one or more visual representations to facilitate patientdiagnosis is generally provided in FIG. 14. The method 280, in oneembodiment, includes accessing one or more patient image deviationscores and one or more patient non-image deviation scores in steps 282and 284, respectively. These deviation scores may be processed, in astep 286, to generate a visual representation of the differencesrepresented by the patient deviation scores. In one embodiment, patientdeviation scores may be derived from dynamic data (e.g., video) orlongitudinal data (e.g., data acquired at discrete points in time over agiven period), and multiple visual representations corresponding todeviations at different points of time may be generated in step 286. Theone or more visual representation may then be output, in a step 288, tofacilitate diagnosis of the patient in a step 290. For deviationsderived from dynamic or longitudinal data, multiple visualrepresentations may be output simultaneously or sequentially.

In some embodiments, the visual representation generally includes acombination and visualization of the various differences represented bythe deviation scores, thus providing a holistic view of patient variancewith respect to standardized data. By way of example, an exemplaryvisual representation 296 is depicted in FIG. 15 in accordance with oneembodiment of the present invention. It is noted, however, the presentlyillustrated embodiment is provided merely for explanatory purposes, andthat other visual outputs may take different forms.

In the presently illustrated embodiment, the visual representation 296includes a region 298 for visualization of patient non-image deviationdata maps, a region 300 for visualization of patient image datadeviation maps or other image data, and a control panel 302. In variousembodiments, numerous display techniques may be used to make thevisualized deviation maps or other results more intuitive to a user, andto more clearly convey the extent of deviation (i.e., abnormality) ofthe results of the specific patient under review. Such displaytechniques, may include, as depicted in the presently illustratedembodiment, color mapping of image pixels or voxels, and color coding ofindividual cells in a table, wherein the color-coded cells eachcorrespond to a particular clinical test result and the color of thecell corresponds to the magnitude of deviation of the patient result incomparison to standardized data. Additional display techniques may alsoinclude temperature gauges, spider graphs, dials, font variations,annotation, and the like.

The exemplary visual representation 296 includes a plurality of cells304, at least some of which include patient non-image deviation mapsassociated with respective clinical test results and are color-coded togive a visual indication of the extent of deviation of the patient fromreference data for each test. For instance, cell 306 may be associatedwith a functional test and shaded in a color that generally representsthe magnitude of the deviation of the result of the functional test forthe patient in comparison to standardized results for the functionaltest. Similarly, cells 308 and 310 may be associated with a cognitivetest and a blood sugar test, respectively, and may be filled withparticular colors to indicate the magnitude of deviations of the patientresults for such tests from standardized result data. Although thepresent illustration depicts discrete color shades for the variouscells, it will be appreciated that a continuous color range may insteadbe used, and that any one or more desired colors may be employed toefficiently communicate the extent of deviation of various clinicaltests to a user. Additionally, it is noted that the patient deviationmaps displayed in the cells 304 may be based on any suitable patienttest having numerical or enumerated results that can be compared tostandardized data, and such maps are not limited to those explicitlydiscussed herein.

Various image data may be displayed in a region 300 of the exemplaryvisual representation 296. In the presently illustrated embodiment, aplurality of structural patient deviation maps 314 and functionalpatient deviation maps 316 are illustrated in the top and bottomportions, respectively, of the region 300. These patient deviation mapsmay include various coloring or shading to denote deviation of a patientanatomical region with respect to standardized data. For instance,regions 318, 320, and 322 in the structural patient deviation maps 314may generally correspond to portions of the patient brain exhibiting noor little deviation from the standardized data, portions exhibitingmoderate deviation, and portions exhibiting severe deviation,respectfully. In embodiments pertaining to the human brain, suchstructural patient deviation maps 314 may include patient corticalthickness deviation maps, which may be generated from MR image data. Itis again noted, however, that the presently disclosed techniques are notlimited to cortical thickness deviation data, or to brain images.Rather, the presently disclosed techniques may be broadly applied tofacilitate quantification, visualization, and diagnosis of a wide arrayof diseases and conditions.

The functional patient deviation maps 316 may also include variouslycolored regions to indicate the magnitude of deviation of that regionfor the patient with respect to standardized data. The functionalpatient deviation maps 316 may include, among other things, cerebralblood flow deviation or metabolic rate deviation of patient data fromthe standardized data, and may, in one embodiment, be generated from PETimage data. In these maps 316, regions 328 may correspond to no orlittle deviation from the standardized data, while regions 330 and 332may signify minor and major deviations, respectively, of the patientfrom the standardized data. The use of three different illustrativeregions in the structural patient deviation maps 314 and functionalpatient deviation maps 316 is used for the sake of clarity and forexplanatory purposes. It should be appreciated that other colors orshading may be used instead of or in addition to those illustratedherein, and such coloring or shading may be provided in a continuousrange or provided at discrete levels.

The control panel 302 may facilitate presentation of other data to auser and user-control of certain visualization processes. For instance,in the presently illustrated embodiment, patient information may bedisplayed in a region 340, population information and selection controlmay be provided in a region 342, and various system parameters, testdata, or other information may be provided in a region 344. In oneembodiment, the population region 342 may allow a user to select aparticular set of standardized data from a library of standardized datagroups based on a desired characteristic. For instance, a user may enterone or more of a desired age range, gender, or race, and the system maythen display visual representations of patient deviations from theselected standardized data set. In other words, in such an embodiment,the user may select demographic characteristics of the populationsegment of the standardized data to which the patient will be comparedfor purposes of visualizing deviation. Consequently, in one embodiment,the user may chose to visualize patient results as a measure ofdeviation from a particular standardized data set demographicallymatched to the patient.

Although the exemplary visual representation 296 includes graphicalrepresentations of structural and functional deviations in image data,as well as deviations with respect to non-image data (e.g., clinicaltests, laboratory tests, and so forth), other visual representationshaving different data, or only subsets of the deviation data visualizedabove, may be generated in other embodiments. For instance, in certainembodiments the generated visual representation may only includerepresentations of deviation with respect to either image data ornon-image data, rather than both.

For example, a visualization method 360 is illustrated in FIG. 16 inaccordance with one embodiment of the present invention. The method 360may include a step 362 of accessing patient image deviation scores formultiple imaging modalities, such as CT, MR, PET, SPECT, digitaltomosynthesis, or the like. The patient image deviation scores may becalculated through a comparison of patient image data to standardizedreference image data pertaining to a population of individuals, asgenerally described above. Further, in various embodiments, the patientimage deviation scores may be computed through comparison of patientstatic image data or patient dynamic image data (e.g., video) acquiredin a single imaging system, or of patient longitudinal image dataacquired over multiple imaging sessions, to reference image data of asimilar or different type (i.e., static, dynamic, or longitudinal). Theaccessed patient image deviation scores may be processed in a step 364to generate a visual representation of patient deviation with respect tothe standardized image data, as generally discussed above. The generatedvisual representation may be output in a step 366 to facilitatediagnosis of the patient in a step 368.

An additional exemplary visualization method 370 is generally depictedin FIG. 17. The method 370 may include accessing patient non-imagedeviation scores for dynamic or longitudinal data in a step 372. Dynamicnon-image data may include a substantially continuous series of clinicaltest results over a given period of time, while non-image longitudinaldata may include test results (or groups of test results) obtained in astaggered fashion (e.g., such as at 3 month intervals) over multipledata acquisition sessions. As generally noted above, the patientnon-image deviation scores for such data may be calculated based oncomparison of patient non-image data to standardized non-image data. Insome embodiments, the patient non-image data on which the deviationscores are based may include non-image data from different modalities(e.g., cognitive data, neurological data, and the like). The patientnon-image deviation scores may be processed in a step 374 to generateone or more visual outputs indicative of deviation of the patientnon-image data from the standardized non-image data. For instance, inone embodiment, a plurality of visual outputs may be generated based oncomparison of a sequence of longitudinal patient non-image data tostandardized non-image data. The visual representations may then beoutput in a step a 376 to facilitate diagnosis of the patient in a step378. Multiple generated visual representations may be outputsimultaneously or sequentially.

FIG. 18 is an exemplary diagram of an automatic comparison workflow 400generally depicting the automatic generation of a severity index forvarious anatomical features of interest. The automatic comparisonworkflow 400 may encompass a number of anatomical features, such asstructural or functional features of a brain, a heart, or the like. Todepict the possibility of such a multitude of anatomical features in acomparison, the automatic comparison workflow 400 is depicted asincluding a first anatomical feature “A” 402, a second anatomicalfeature “B” 404, a third anatomical feature “C” 406, an “N'th”anatomical feature “N” 408, and so forth. The automatic workflowcomparison of FIG. 18 represents a specific implementation of the moregeneralized matching and presentation techniques described in U.S.Patent Application Publication No. 2007/0078873 A1, published on Apr. 5,2007, and entitled “COMPUTER ASSISTED DOMAIN SPECIFIC ENTITY MAPPINGMETHOD AND SYSTEM,” which is hereby incorporated by reference in itsentirety. For example, in this specific implementation the variousanatomical features 402, 404, 406, 408 represent various axes while thedisease severity deviation maps 410, 412, 414, 416 discussed belowrepresent different labels associated with each axis, and so forth.

For each anatomical feature, a number of deviation maps havingvariations in the extent, or severity level, of a disease or a conditionare provided. For example, for anatomical feature “A” 402, a number ofreference deviation maps 410 having variations in the extent of adisease or a condition associated with anatomical feature “A” areprovided. Similarly, sets of reference deviation maps 412, 414, and 416are provided, which exhibit the variations in the extent of a disease orcondition for each of the remaining respective anatomical featuresthrough the Nth feature. As will be appreciated by those of ordinaryskill in the art, each of the disease severity reference deviation mapswithin the respective map sets 410, 412, 414, 416 are generated for therespective anatomical feature 402, 404, 406, 408 and, in the case ofimage data (rather than non-image data) reference deviation maps, may befurther categorized by a tracer or tracers (if one was employed) and bythe imaging technology employed. For example, reference deviation mapswithin the respective deviation map sets 410, 412, 414, 416 may begenerated by magnetic resonance (MR) imaging, positron emissiontomography (PET), computed tomography (CT), single photonemission-computed tomography (SPECT), ultrasound, optical imaging, orother conventional imaging techniques and by using suitable tracers inappropriate circumstances. As discussed above, the reference deviationmaps may also or instead be generated from non-image data, includingclinical data.

For each anatomical feature, the disease severity reference deviationmaps 410, 412, 414, 416 of the anatomical features are ordered, asgenerally indicated by arrow 418, according to the severity of thedisease or condition or otherwise associated with a severity of thedisease or condition. For example, for anatomical feature “A” 402, thedisease severity reference deviation maps 410 may be ordered inascending order from the least extent or amount of the disease orcondition, to the highest amount or extent of the disease or condition.

In the depicted embodiment, eight reference deviation maps are depictedin each of disease severity deviation map groups 410, 412, 414, 416 asrepresenting the various disease severity levels associated with eachanatomical feature 402, 404, 406, 408. As will be appreciated by thoseof ordinary skill in the art, however, the number of reference deviationmaps in the sets of disease severity deviation maps 410, 412, 414, 416is arbitrary and can be increased or decreased depending on theimplementation and the characteristics of the reviewer. For example, inexemplary embodiments where the comparison process is automated, thenumber of reference maps within each of the groups of disease severitydeviation maps 410, 412, 414, 416 may contain more than eight maps, suchas ten, twenty, one hundred, and so forth. Further, though a singledisease severity reference deviation map is presently depicted ascorresponding to each ordered severity level for each anatomicalfeature, each degree of severity for each anatomical feature mayactually have one or more than one disease severity reference deviationmap provided for comparison. For example, in exemplary implementationswhere the comparison process is automated, each severity level orseverity index for an anatomical feature 402, 404, 406, 408 may berepresented by more than one disease severity reference deviation map.

Various patient deviation maps 420 may then be evaluated relative to therespective disease severity reference deviation maps 410, 412, 414, 416to determine an extent of disease or condition in the patient deviationmaps 420 in comparison to the respective disease severity referencedeviation maps. Each patient deviation map 420 for an anatomical featuremay be generated by comparing acquired patient data to normativestandardized anatomical data for the respective anatomical feature. Aswill be appreciated by those of ordinary skill in the art, the patientdeviation maps 420 may be derived from images acquired using one or moresuitable tracers (e.g., when needed to capture desired functionalinformation), from images acquired through other techniques, or fromnon-image data, as described above. Therefore, in an exemplaryembodiment, the patient deviation maps 420 based on image data are notonly compared to a set of disease severity reference deviation maps 410,412, 414, 416 corresponding to the same anatomical feature 402, 404,406, 408, but also to those reference maps in the set of diseaseseverity reference deviation maps 410, 412, 414, 416 generated fromimage data acquired using the same or a comparable tracer or tracers, ifpresent, and using the same or a comparable imaging technology. In anexemplary embodiment, the comparison between the one or more maps ofpatient deviation maps 420 and the respective set of disease severityreference deviation maps 410, 412, 414, 416 is performed automatically,such as by pattern matching or other suitable comparison techniques androutines.

For example, in one implementation patient deviation maps 420 generatedfrom image data corresponding to the anatomical feature “A” 402 may beautomatically compared to the corresponding set of ordered diseaseseverity reference deviation maps 410 that were generated from dataacquired using the same tracer or tracers, if a tracer was employed, andusing the same imaging modality, such as MR or PET. As will beappreciated by those of ordinary skill in the art, patient deviationmaps 420 and the respective disease severity reference deviation maps410, 412, 414, 416 to which they are compared may vary depending onpatient specific factors (such as patient history, patient symptoms, andso forth) as well as clinical factors (such as standard practice for theattending physician and for the medical facility, preliminary diagnoses,years of practice, and so forth).

In the presently illustrated example, each comparison generates aseverity index 422 that expresses or represents the extent of disease inthe respective patient deviation map 420, as determined by comparison tothe anatomical feature-specific disease severity reference deviationmaps 410, 412, 414, 416. As will be appreciated by those of skill in theart, in those embodiments in which the comparison is performedautomatically, the severity index 422 may also be generatedautomatically. In such embodiments, a reviewer or evaluator may simplybe provided with a severity index 422 for each anatomical feature ofinterest or for which patient deviation maps 420 were generated orprocessed.

In some embodiments, an aggregate patient severity score 424 isgenerated from the severity indices 422 using statistical analysis 426,such as a rules-based aggregation method or technique. In an exemplaryembodiment, the aggregate severity score 424 is generated automatically,such as by automatic implementation of the analysis 426 using suitableroutines or computer-implemented code. In such embodiments, a revieweror evaluator may simply be provided with an overall or aggregateseverity score for the patient.

In addition to calculating disease severity scores or indices for apatient with respect to a single disease type, the presently discloseddata processing system may also calculate a combined disease severityscore based on a plurality of different disease types and severitylevels. For instance, an exemplary method 430 for determining a combineddisease severity score for a patient based on multiple disease types andseverity levels is depicted in FIG. 19 in accordance with one embodimentof the present invention. The method 430 may include a step 432 ofaccessing reference deviation data (such as reference deviation maps orother data) for multiple disease types. Such reference deviation mapsmay be standardized according to a demographic (or other)characteristic. Additionally, the step 432 may also include accessingreference deviation maps or data with respect to a plurality of severitylevels for one or more of the disease types. In one embodiment, thereference deviation data may include functional or structural deviationmaps indicative of differences between normal individuals andindividuals diagnosed with particular disease types, or diagnosed withseverity levels of the different disease types. Disease severity scoresmay be associated with subsets of the reference deviation data, such asthe different reference deviation maps associated with various severitylevels, as generally discussed above. These individual disease severityscores may also be accessed in a step 434.

The method 430 may also include selecting patient disease severitylevels in a step 436. Selection of patient disease severity levels maybe performed in a variety of manners. In one embodiment, a user maycompare a patient deviation map to a library or database of knowndeviation maps indicative of functional or structural deviationassociated with various disease types and/or severity levels. Anexemplary visual reference library 484 of known, standardized deviationmaps corresponding to normal patients and patients diagnosed withvarious disease types, is generally illustrated in, and discussed ingreater detail below with respect to, FIGS. 21 and 22. In such anembodiment, the user may compare a patient deviation map to thosereference deviation maps included in the library 484 to diagnose thepatient as having one or both of a particular disease type and severitylevel. To facilitate such manual analysis and comparison, in oneembodiment, one or more of the reference deviation maps or patientdeviation maps may be displayed by a computing system, and a user mayindicate (via a user-interface) a selection of a particular severitylevel for each disease type corresponding to the reference deviation mapclosest to the patient deviation map.

In another embodiment, a computing system, such as the data processingsystem 34, may be programmed to automatically compare the patientdeviation map to reference deviation maps in the library of referencedeviation maps and to automatically select the closest matches.Alternatively, various disease scores may be calculated based on givendiseases and severity levels and compared to a patient disease score toautomatically determine and select the closest match. In yet anotherembodiment, a computing system may apply an algorithm to select a subsetof the reference deviation maps, from which a user may make the finalselections.

Following selection of patient severity levels for a plurality ofdisease types, a combined disease severity score may be automaticallycalculated in step 438. Finally, a report including or based on thecombined disease severity score may be output in a step 440. Asgenerally noted above, outputting of the report, as well as otherreports and data described herein, may include outputting the report tomemory, outputting the report to a user, or outputting the report to adifferent software routine for further processing.

The method 430 described above may be employed in connection with avariety of anatomical regions and disease types, including, but notlimited to, brain disorders. An exemplary process for evaluating suchbrain disorders may be better understood with reference to block diagram450, which is illustrated in FIG. 20 in accordance with one embodimentof the present invention. Patient image data 454 and patient non-imagedata 456 may be collected from a patient 452. As noted elsewhere herein,such patient image data may include images obtained through any ofvarious imaging modalities, and may include patient cortical thicknessmaps, patient cortical thickness deviation maps or any other desiredimage data. As also previously discussed, the patient non-image data 456may include numerous data types and information, such as results ofclinical tests and laboratory tests, family history, genetic history,and so forth. Based on the patient image data 454, it may be determinedthat the patient 452 has a vascular disease, as generally indicated inblock 458. Such a determination or diagnosis may be output in a report460. The patient image data 454 and the patient non-image data 456 mayalso be used to determine whether the patient 452 has aneurodegenerative disease, as generally indicated in block 462.

Block 464 generally represents a work flow for determining patientseverity levels for a plurality of brain disorders or disease types 466.Separate pluralities of reference deviation maps 468 may be associatedwith each disease type, and each plurality may generally representdifferent severity levels of its respective disease type. Further, eachreference deviation map may be associated with a disease severity score(e.g., of the series S1 . . . SN for each disease type). For example, inone embodiment, the reference deviation map representative of the lowestseverity level of a particular disease may be associated with the lowestdisease severity score (i.e., S1) for that disease type, while otherreference deviation maps indicative of increasing severity levels of thedisease type may be associated with increasing disease severity scores(i.e., S2, S3, . . . , SN). Either or both of patient image data 454 andpatient non-image data 456 may be compared (block 470) to the sequenceof reference deviation maps for disease type A to determine a patientseverity level 472 for disease type A. The individual patient severityscore XA for disease type A may equal the disease severity scoreassociated with the reference deviation map for disease type A closestto the patient data to which it is compared. Alternatively, if thepatient data suggests that the patient severity falls somewhere betweentwo of the reference deviation maps for disease type A, the individualpatient severity score XA may be computed from the two disease severityscores associated with the individual reference deviation maps closestto the patient data. The individual severity scores for other diseasetypes may be calculated in a similar manner based on their ownassociated reference deviation maps.

Once the individual patient severity scores 472 for each disease type iscalculated, such individual scores may be utilized to calculate acombined patient disease severity score, as generally shown in block474. The combined patient disease severity score may be calculatedthrough addition of the individual patient severity scores, averaging ofthe individual patient severity scores (which may be weighted asdesired), or in any other suitable fashion. Further, the combinedpatient disease severity score may also indicate the relativecontribution of each disease type to a patient condition. For instance,the combined patient disease severity score may indicate thatAlzheimer's disease is the primary contributing factor to patientdementia or some other condition. In another embodiment, the combinedpatient disease severity score may indicate the relative contribution ofeach of a plurality of disease types to a patient condition. By way ofexample, the combined patient disease severity score may indicate therelative contribution of various brain disorders to patient dementia(e.g., 40% AD, 30% FTD, 30% other). A report 476 based on or indicativeof the combined patient disease severity score may be output to a useror to storage.

As noted above, reference images and deviation maps of an exemplaryvisual reference library 484 are depicted in FIGS. 21 and 22 inaccordance with one embodiment of the present invention. It is notedthat the presently depicted representations are merely provided forillustrative purposes, and that an actual implementation of a visualreference library may include different or additional images. Indeed,various embodiments of a visual reference library 484 may include asignificantly greater number of images, such as tens, hundreds, or evengreater numbers of reference images or maps, which may be standardizedin various embodiments as discussed above. It will be furtherappreciated that images within the visual reference library 484 may beobtained via one or any number of imaging modalities, and may includeoriginal images, deviation maps such as those discussed above, or anyother suitable reference images. In the presently illustratedembodiment, the reference images generally denote metabolic ratedeviations between normal individuals and individuals diagnosed withvarious brain disorders. In other embodiments, however, other deviationmaps, such as cortical thickness deviation maps, cerebral blood flowrate deviation maps, or even deviation maps for non-brain anatomies, maybe included in the visual reference library 484.

In the presently illustrated embodiment, the visual reference library484 is depicted as including a set of reference images 486 for normalpersons, and reference deviation maps 488 and 490 corresponding topatients clinically diagnosed with mild and severe forms, respectively,of Alzheimer's disease (AD). The visual reference library 484 may alsoinclude deviation maps 492 corresponding to patients diagnosed withdiffuse cortical Lewy body disease (DLBD) and deviation maps 494representative of patients clinically diagnosed with frontotemporaldementia (FTD). The visual reference library 484 may also includeadditional deviation maps, such as maps 496 associated with progressivesupranuclear palsy (PSP), maps 498 associated with multi-infarctdementia (MID), and maps 500 associated with normal pressurehydrocephalus (NPH).

Technical effects of one or more embodiments of the present inventionmay include the diagnosis of various patient disease types and severitylevels, as well as providing decision support tools for user-diagnosisof patients. In one embodiment, technical effects include thevisualization of patient clinical image and non-image informationtogether in a holistic, intuitive, and uniform manner, facilitatingefficient diagnosis by an observer. In another embodiment, technicaleffects include the calculation of patient cortical deviation maps andreference cortical deviation maps of known brain disorders, thecalculation of additional patient and reference deviation maps, and thecombination of such maps with other clinical tests, to enablequantitative assessment and diagnosis of brain disorders.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

A technical contribution for the disclosed method and apparatus is thatis provides for a computer implemented system and method for analyzingand visualizing local clinical features.

One skilled in the art will appreciate that embodiments of the inventionmay be interfaced to and controlled by a computer readable storagemedium having stored thereon a computer program. The computer readablestorage medium includes a plurality of components such as one or more ofelectronic components, hardware components, and/or computer softwarecomponents. These components may include one or more computer readablestorage media that generally stores instructions such as software,firmware and/or assembly language for performing one or more portions ofone or more implementations or embodiments of a sequence. These computerreadable storage media are generally non-transitory and/or tangible.Examples of such a computer readable storage medium include a recordabledata storage medium of a computer and/or storage device. The computerreadable storage media may employ, for example, one or more of amagnetic, electrical, optical, biological, and/or atomic data storagemedium. Further, such media may take the form of, for example, floppydisks, magnetic tapes, CD-ROMs, DVD-ROMs, hard disk drives, and/orelectronic memory. Other forms of non-transitory and/or tangiblecomputer readable storage media not list may be employed withembodiments of the invention.

A number of such components can be combined or divided in animplementation of a system. Further, such components may include a setand/or series of computer instructions written in or implemented withany of a number of programming languages, as will be appreciated bythose skilled in the art. In addition, other forms of computer readablemedia such as a carrier wave may be employed to embody a computer datasignal representing a sequence of instructions that when executed by oneor more computers causes the one or more computers to perform one ormore portions of one or more implementations or embodiments of asequence.

Therefore, in accordance with one embodiment, a computer readablestorage medium has stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toaccess a set of patient image data acquired from a patient and identifya target region of interest (ROI) from the set of patient image data.The instructions also cause the computer to define a feature datasetfrom the set of patient image data, the feature dataset representing alocal feature of interest extracted from the target ROI, access a set ofpre-computed image data, and define a reference dataset from the set ofpre-computed image data, the reference dataset representing an expectedvalue of the local feature of interest for the target ROI. Further, theinstructions cause the computer to compare the feature dataset to thereference dataset, generate a deviation metric based on the comparison,and output a visualization of the deviation metric.

In accordance with another embodiment, a method includes accessing aclinical image dataset comprising clinical image data acquired from apatient, identifying a first ROI from the clinical image dataset, andextracting at least one local feature corresponding to the first ROI.The method also includes defining a local feature dataset comprisingdata representing the at least one local feature, accessing apre-computed reference dataset comprising image data representing anexpected value of the at least one local feature, and comparing thelocal feature dataset to the pre-computed reference dataset. Further,the method includes calculating at least one deviation metric from thecomparison and outputting a visualization of the at least one deviationmetric.

In accordance with yet another embodiment, a system for analyzing imagedata includes a patient database having stored thereon image dataacquired from a patient, a reference database having stored thereonpre-computed image data acquired from a reference population, and aprocessor. The processor is programmed to access a set of patient datafrom the patient database, identify a ROI from the set of patient data,extract a local feature corresponding to the ROI, and define a set ofpatient feature data from the set of patient data representing the localfeature. The processor is further programmed to access a set ofpre-computed feature data from the reference database representing thelocal feature, compare the set of patient feature data to the set ofpre-computed feature data, generate a deviation map from the comparison,and output a visualization of the deviation map. The system furtherincludes a GUI configured to display the deviation map for the localfeature.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. A non-transitory computer readable storage mediumhaving stored thereon a computer program comprising instructions, which,when executed by a computer, cause the computer to: access a set ofpatient image data acquired from a patient; identify a target region ofinterest (ROI) from the set of patient image data; derive a quantitativeparameter from image data representing a plurality of pixels within thetarget ROI, the quantitative parameter representing a local feature ofinterest of the target ROI; access a set of pre-computed image data;define a reference dataset from the set of pre-computed image data, thereference dataset representing an expected value of the local feature ofinterest for the target ROI; identify a number of standard deviationsbetween the quantitative parameter and the reference dataset; calculatea deviation metric based on the identified number of standarddeviations; and display the deviation metric.
 2. The computer readablestorage medium of claim 1 wherein the instructions further cause thecomputer to define the quantitative parameter as one of a shape-basedparameter, a size-based parameter, a texture-based parameter, and amaterial-based parameter.
 3. The computer readable storage medium ofclaim 1 wherein the instructions further cause the computer to definethe reference dataset as a subset of the set of pre-computed image datacomprising image data corresponding to the target ROI.
 4. The computerreadable storage medium of claim 1 wherein the instructions furthercause the computer to identify the local feature of interest as at leastone of an anatomical characteristic and a functional characteristic ofthe target ROI.
 5. The computer readable storage medium of claim 1wherein the instructions further cause the computer to: display an imagecorresponding to the set of patient image data; and visually distinguishthe first ROI within the image.
 6. The computer readable storage mediumof claim 1 wherein the instructions further cause the computer toautomatically identify the first ROI using an automated computer-basedalgorithm.
 7. A method comprising: accessing a clinical image datasetcomprising clinical image data acquired from a patient; identifying afirst region of interest (ROI) from the clinical image dataset;extracting at least one local feature corresponding to the first ROI,each of the at least one extracted local features comprising aquantitative parameter derived from clinical image data representing aplurality of pixels within the first ROI; accessing a pre-computedreference dataset comprising image data representing an expected valueof the at least one local feature; calculating at least one deviationmetric comprising a z-score deviation between the quantitative parameterof a respective extracted local feature and the pre-computed referencedataset; and outputting the at least one deviation metric.
 8. The methodof claim 7 wherein accessing the pre-computed reference datasetcomprises accessing a dataset corresponding to derived local features ofone of known normals and known abnormals.
 9. The method of claim 7further comprising identifying the at least one local feature as atleast one of an anatomical characteristic and a functionalcharacteristic of the first ROI.
 10. The method of claim 7 furthercomprising identifying the at least one local feature as a quantitativevalue comprising at least one of a shape-based parameter, a size-basedparameter, a texture-based parameter, and a material-based parameter.11. The method of claim 7 further comprising: displaying an imagecorresponding to the clinical image dataset; drawing a contour on theimage to identify the first ROI; color-coding the at least one deviationmetric to indicate an extent of deviation between the clinical imagedataset and the pre-computed reference dataset; and displaying thecolor-coded at least one deviation metric on the first ROI.
 12. Themethod of claim 7 further comprising automatically identifying the ROIusing an automated computer-based algorithm.
 13. The method of claim 7further comprising: extracting a first local feature comprising aquantitative parameter of a first type; extracting a second localfeature comprising a quantitative parameter of a second type, differentfrom the first type; calculating a first deviation metric comprising afirst z-score deviation between the quantitative parameter of the firsttype and the pre-computed reference dataset; calculating a seconddeviation metric comprising a second z-score deviation between thequantitative parameter of the second type and the pre-computed referencedataset; normalizing the first and second deviation metrics to a commoncolor scale; displaying the first deviation metric on a color-coded gridin a first color of the common color scale; and displaying the seconddeviation metric on the color-coded grid in a second color of the commoncolor scale.
 14. The method of claim 7 further comprising identifying asecond ROI from the clinical image dataset.
 15. The method of claim 14further comprising: extracting a first local feature from the first ROI,the first local feature comprising a quantitative parameter derived fromclinical image data representing a plurality of pixels within the firstROI; and extracting a second local feature from the second ROI, thesecond local feature comprising a quantitative parameter derived fromclinical image data representing a plurality of pixels within the secondROI.
 16. A system for analyzing image data comprising: a patientdatabase having stored thereon image data acquired from a patient; areference database having stored thereon pre-computed image dataacquired from a reference population; a processor programmed to: accessa set of patient data from the patient database; identify a region ofinterest (ROI) from the set of patient data; extract a plurality oflocal features corresponding to the ROI, wherein each local featurecomprises a quantitative parameter derived from a subset of the set ofpatient data representing a plurality of pixels within the ROI; access aset of pre-computed feature data from the reference databaserepresenting baseline data for the plurality of local features;calculate deviation metrics for the plurality of local features, eachdeviation metric comprising a quantitative deviation between aquantitative parameter of a respective local feature and data within theset of pre-computed feature data; generate a deviation map from thedeviation metrics; and output the deviation map; and a graphical userinterface (GUI) configured to display the deviation map for theplurality of local features.
 17. The system of claim 16 wherein thereference database comprises image data corresponding to derived localfeatures of one of known normals and known abnormals.
 18. The system ofclaim 16 wherein the patient database comprises image data acquireduring one of a single scan and a consecutive series of scans; andwherein the processor is further programmed to calculate the pluralityof deviation metrics using a z-score calculation.
 19. The system ofclaim 16 wherein the processor is further programmed to identify theplurality of local features as one of at least one of a shape-basedparameter, a size-based parameter, a texture-based parameter, and amaterial-based parameter.
 20. The system of claim 16 wherein the GUIcomprises a display of the plurality of deviation metrics in a codedgrid.
 21. The system of claim 16 wherein the processor is furtherprogrammed to: normalize the plurality of deviation metrics; define acommon color scale that represents the normalized plurality of deviationmetrics, a first end of the common color scale corresponding to a smallquantitative deviation between the set of patient data and the set ofpre-computed feature data, and a second end of the common color scalecorresponding to a large quantitative deviation between the set ofpatient data and the set of pre-computed feature data; and assign acolor from the common color scale to each of the normalized plurality ofdeviation metrics; and wherein a first deviation metric of the pluralityof deviation metrics represents a first type of quantitative parameterand a second deviation metric of the plurality of deviation metricsrepresents a second type of quantitative parameter, different from thefirst type.
 22. The system of claim 21 wherein the GUI comprises adisplay of the normalized plurality of deviation metrics with theassigned colors.
 23. The computer readable storage medium of claim 1wherein the instructions further cause the computer to calculate thedeviation metric as a z-score.
 24. The computer readable storage mediumof claim 1 wherein the instructions further cause the computer to:register the set of pre-computed image data to the set of patient data;and define the reference dataset from the registered set of pre-computedimage data.